ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Molecular Detection of SARS-CoV-2 in Formalin Fixed Paraffin Embedded Specimens
This article has 6 authors:Reviewed by ScreenIT
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More prevalent, less deadly? Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling
This article has 8 authors:Reviewed by ScreenIT
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Identification of super-transmitters of SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Aerosolized Hydrogen Peroxide Decontamination of N95 Respirators, with Fit-Testing and Viral Inactivation, Demonstrates Feasibility for Reuse during the COVID-19 Pandemic
This article has 11 authors:Reviewed by ScreenIT
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Psychological impact of infectious disease outbreaks on pregnant women: rapid evidence review
This article has 3 authors:Reviewed by ScreenIT
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Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA
This article has 9 authors:Reviewed by ScreenIT
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Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources
This article has 4 authors:Reviewed by ScreenIT
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Explaining among-country variation in COVID-19 case fatality rate
This article has 3 authors:Reviewed by ScreenIT